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Proceedings Paper

Hyperspectral image band selection based on genetic algorithm
Author(s): Jiping Ma; Zhaobao Zheng; Qingxi Tong; Lanfen Zheng; Bin Zhang
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Paper Abstract

Optimum band selection for visual interpretation and classification is an interesting task in conventional remote sensing, and, as an effective means to mitigate the curse of dimensionality, which has assumed growing importance with the availability of hyperspectral remote sensing data. In determining three-channel combination for a informative display in an image-cube and determining feature combination for fast classification, band selection is regarded indispensable in hyperspectral remote sensing. When applied to data acquired from a hyperspectral sensor, which is usually with a set of hundreds of band, however, conventional band selection procedure, of any criterion, becomes not viable with respect to the particularly time consuming. To cope with this pitfall, a method based upon genetic algorithm is proposed in this paper. An experiment, with a 121 band data set, demonstrate the efficiency. For simplification, the algorithm is designed to choose a combination which produces the most informative visual result when used as the top color preference in an image- cube. With little modification in criterion, the algorithm can be used to select features for classification purpose. The corresponding result is also presented in this paper.

Paper Details

Date Published: 25 September 2001
PDF: 4 pages
Proc. SPIE 4548, Multispectral and Hyperspectral Image Acquisition and Processing, (25 September 2001); doi: 10.1117/12.441403
Show Author Affiliations
Jiping Ma, Wuhan Univ. and Institute of Remote Sensing Applications (China)
Zhaobao Zheng, Wuhan Univ. and Institute of Remote Sensing Applications (China)
Qingxi Tong, Wuhan Univ. and Institute of Remote Sensing Applications (China)
Lanfen Zheng, Wuhan Univ. and Institute of Remote Sensing Applications (China)
Bin Zhang, Wuhan Univ. and Institute of Remote Sensing Applications (China)


Published in SPIE Proceedings Vol. 4548:
Multispectral and Hyperspectral Image Acquisition and Processing

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